Particle Jets Collection of particles from common source

Particle Jets Collection of particles from common source
Several sources in each collision Hard scattering, multiple parton interactions in the underlying event, initial and final state radiation Describe the simulated collision viewed with a microscope (idealized) Microscope technology – jet finding algorithm Resolution – ability of a jet finder to (spatially) resolve jet structures of collision, typically a configuration parameter of the jet finder Sensitivity – kinematic threshold for particle bundle to be called a jet, another configuration parameter of the jet finder

Usefulness of Particle Jets
Good reconstruction reference for detector jets Provide a truth reference for the reconstucted jet energy and momentum E.g., can be used in simulations together with fully simulated detector jets to calibrate those (we will follow up on this point later!) Extract particle jets from measurement by calibration and unfolding signal characteristics from detector jets Understand effect of experimental spatial resolution and signal thresholds at particle level Remember: electromagnetic and hadronic showers have lateral extension → diffusion of spatial particle flow by distributing the particle energy laterally! Remember: noise in calorimeter imply a “useful” signal threshold → may introduce acceptance limitations for particle jets! Good reference for physics Goal of all selection and unfolding strategies in physics analysis Reproduce particle level event from measurement as much as possible! Require correct simulations of all aspects of particle spectrum of collision right Matrix element, parton showers, underlying event (non-pertubative soft QCD!), parton density functions,… Parton shower matching to higher order matrix calculation in complex pp collision environment is a hot topic among theorists/phenomenologists today! Allow to compare results from different experiments Specific detector limitations basically removed Also provides platform for communication with theorists (LO and some NLO ) Important limitations to be kept in mind NLO particle level generators not available for all processes (more and more coming) NNLO etc. not in sight

What Is Jet Reconstruction, Then?
Model/simulation: particle jet Attempt to collect the final state particles described above into objects (jets) representing the original interaction features/parton kinematic Re-establishing the correlations induced by the common source at a given spatial resolution Experiment: detector jet Attempt to collect the detector signals from these particles to measure their original kinematics Usually no attempt to reconstruction a (very model/order of calculation dependent) parton level!

Image of Jets in the Detector

Detector Effects On Jets
Change of composition Radiation and decay inside detector volume “Randomization” of original particle content Defocusing changes shape in lab frame Charged particles bend in solenoid field Attenuation changes energy Total and partial loss of soft charged particles in magnetic field Partial and total energy loss of charged and neutral particles in inactive upstream material Hadronic and electromagnetic cacades in calorimeters Distribute energy spatially Lateral particle shower overlap 100 MeV 10 GeV 1 GeV

Detector Effects On Jets
Change of composition Radiation and decay inside detector volume “Randomization” of original particle content Defocusing changes shape in lab frame Charged particles bend in solenoid field Attenuation changes energy Total loss of soft charged particles in magnetic field Partial and total energy loss of charged and neutral particles in inactive upstream material Hadronic and electromagnetic cacades in calorimeters Distribute energy spatially Lateral particle shower overlap 100 MeV 10 GeV 1 GeV

Calorimeter Acceptance In Magnetic Field
Single particle response modification Magnetic field in front of calorimeter Charged particles may not reach calorimeter at all

Single particle response modification
Acceptance Single particle response modification Magnetic field in front of calorimeter Charged particles may not reach calorimeter at all

Jet Reconstruction Challenges & Tasks

Jet Reconstruction Challenges
Experiment (“Nature”) Jet Reconstruction Challenges longitudinal energy leakage detector signal inefficiencies (dead channels, HV…) pile-up noise from (off- and in-time) bunch crossings electronic noise calo signal definition (clustering, noise suppression…) dead material losses (front, cracks, transitions…) detector response characteristics (e/h ≠ 1) jet reconstruction algorithm efficiency lost soft tracks due to magnetic field added tracks from underlying event added tracks from in-time (same trigger) pile-up event jet reconstruction algorithm efficiency physics reaction of interest (interaction or parton level)

Experiment (“Nature”) Jet Reconstruction Challenges longitudinal energy leakage detector signal inefficiencies (dead channels, HV…) pile-up noise from (off- and in-time) bunch crossings electronic noise calo signal definition (clustering, noise suppression…) dead material losses (front, cracks, transitions…) detector response characteristics (e/h ≠ 1) jet reconstruction algorithm efficiency lost soft tracks due to magnetic field jet calibration task is to unfold all this to reconstruct the particle level jet driving the signals… added tracks from underlying event added tracks from in-time (same trigger) pile-up event jet reconstruction algorithm efficiency physics reaction of interest (interaction or parton level)

Experiment (“Nature”) Jet Reconstruction Challenges longitudinal energy leakage detector signal inefficiencies (dead channels, HV…) pile-up noise from (off- and in-time) bunch crossings electronic noise calo signal definition (clustering, noise suppression…) dead material losses (front, cracks, transitions…) detector response characteristics (e/h ≠ 1) jet reconstruction algorithm efficiency lost soft tracks due to magnetic field jet calibration task is to unfold all this to reconstruct the particle level jet driving the signals… …modeling and calculations establish the link between particle and interaction level… added tracks from underlying event added tracks from in-time (same trigger) pile-up event jet reconstruction algorithm efficiency physics reaction of interest (interaction or parton level)

Experiment (“Nature”) Jet Reconstruction Challenges longitudinal energy leakage detector signal inefficiencies (dead channels, HV…) pile-up noise from (off- and in-time) bunch crossings electronic noise calo signal definition (clustering, noise suppression…) dead material losses (front, cracks, transitions…) detector response characteristics (e/h ≠ 1) jet reconstruction algorithm efficiency lost soft tracks due to magnetic field jet calibration task is to unfold all this to reconstruct the particle level jet driving the signals… …modeling and calculations establish the link between particle and interaction level… added tracks from underlying event added tracks from in-time (same trigger) pile-up event jet reconstruction algorithm efficiency …but how is this really done? physics reaction of interest (interaction or parton level)

Jet Reconstruction Task
Experiment (“Nature”) The experiment starts with the actual collision or the generator… Triggered collision with signal parton collision, fragmentation & underlying event (experiment), or: Interaction level calculation with fragmentation and underlying event modeling (simulations) … go to the particles in the simulation … Here particle level event represent the underlying interaction and the full complexity of the physics of the collision in the experiment … collect the detector signals … From the readout (experiment), or: Take the stable (observable) particles and simulate the signals in the detector (e.g., the calorimeter and tracking detector)(simulations) … and compare them! Complex – need to include all experimental biases like event selection (trigger bias), topology and detector inefficiencies This establishes particle jet references for the detector jets! Of course only in a statistical sense, i.e. at the level of distributions!

Experiment (“Nature”) Modeling Particle Jets Particles UE Fragmentation MB Multiple Interactions Stable Particles Decays Jet Finding Particle Jets Generated

Experiment (“Nature”) Modeling Calorimeter Jets Reconstructed Jets Stable Particles Raw Calorimeter Signals Detector Simulation Reconstructed Calorimeter Signals Signal Reconstruction Jet Finding Identified Particles

Experiment (“Nature”) Measuring Calorimeter Jets Reconstructed Jets Observable Particles Raw Calorimeter Signals Measurement Reconstructed Calorimeter Signals Signal Reconstruction Jet Finding Identified Particles

Jet Calibration What is jet calibration? Why is it needed?
Straight forward: attempt to reconstruct a measured jet such that its final four-momentum is close to the true jet kinematics generating the signal Why is it needed? Could compare simulated and measured calorimeter signals at any scale and deduct the true kinematics from the corresponding particle jet in simulation Remember energy scales in calorimeters? But need to reconstruct any jet in the experiment Even (or especially) the ones in events we have not simulated – which probably means new physics? To understand these events the best measurement of the true jet independent of the availability of simulations for this specific event – no simulation bias allowed in general! Can we calibrated without simulations at all? Complex physics and detector environment – hard to avoid simulations for precision reconstruction! But there are in-situ jet calibrations So jet reconstruction needs to include a calibration Use a simulated calibration sample representing simple final state Chose a somewhat understood Standard Model topology like QCD di-jets Calibrate using measurable jet features Establish functions using jet observables as parameters to calibrate calorimeter jets from a basic scale to the final jet energy scale If done right, simulation biases can be reduced, especially concerning the correct simulation of the event topology Understand the limitations (systematic error) in the context of the analysis All this is the global subject of the remaining lectures!

Global Hadronic Energy Scale

Global Calibration Techniques
Use jet context for cell calibration Determine cell weights using jet energy constraints Same principle idea as for local cell weighting, but different global energy scale Needs jet truth reference Jet context relevant Supports assumption of hadronic signal activity Has enhanced electromagnetic component contributing to the weighting function parameterizations of all cells – larger (volume/area) context than topological clustering May be biased with respect to calorimeter signal definition and jet algorithms Jet energy references for calorimeter jets Simulation Matching particle level jet (same jet definition) energy Experiment pT balance with electromagnetic system like photon or Z-boson W mass spectroscopy Sampling energy based jet calibration Coarser than cell signals but less numerical complexity Fewer function parameters

Simulated particle jets
Truth Jet Matching Simulated particle jets Establish “true” energy reference to constrain calibration function fits for calorimeter jets Attempt to reconstruct true jet energy Need matching definition Geometrical distance Isolation and unique 1-to-1 jet matching

Global Calibration Fits Using Simulations
Select matched jet pair Typically small matching radius Rmatch = 0.2 – 0.3 Restrict jet directions to regions with good calorimeter response No excessive dead material Away from cracks and complex transition geometries Calibration functions Cell signal weighting Large weights for low density signals Small weights for high density signals Sampling layer signal weighting Weights determined by longitudinal energy sharing in calorimeter jet Functions can be complex Often highly non-linear systems Example of calorimeter regions to be considered for jet calibration fits in ATLAS (tinted green). The red tinted regions indicate calorimeter cracks and transitions. The points show the simulated jet response on electro-magnetic energy scale, as function of the jet pseudorapidity. (figure for illustration purposes only!)

Select matched jet pair Typically small matching radius Rmatch = 0.2 – 0.3 Restrict jet directions to regions with good calorimeter response No excessive dead material Away from cracks and complex transition geometries Calibration functions Cell signal weighting Large weights for low density signals Small weights for high density signals Sampling layer signal weighting Weights determined by longitudinal energy sharing in calorimeter jet Functions can be complex Often highly non-linear systems

Fitting Possible constraints Resolution optimization Signal linearity Combination of both Regularization of calibration functions Try to linearize function ansatz Use polynomials Can reduce fits to solving system of linear equations Non-linear function fitting Use numerical approaches to find (local) minimum for multi-dimensional test functions (e.g., software like MINUIT etc.)

Attempted de-convolution of signal contributions Normalization choice convolutes various jet response features E.g., cell weights correct for dead material and magnetic field induced energy losses, etc. Limited de-convolution Fit corrections for energy losses in material between calorimeter modules with different functional form Separation in terms, but still a correlated parameter fit

Attempted de-convolution of signal contributions Normalization choice convolutes various jet response features E.g., cell weights correct for dead material and magnetic field induced energy losses, etc. Limited de-convolution Fit corrections for energy losses in material between calorimeter modules with different functional form Separation in terms, but still a correlated parameter fit Relatively low level of factorization in this particular approach with correlated (by combined fit) parameters!

Jet Inputs & Images

Experimental Jet Finder Input In ATLAS
Calorimeter towers Noise-suppressed towers Cells in towers from topological clusters EM scale only Photon/hadron response imbalanced during jet formation Least algorithm bias Calorimeter cell clusters EM scale option Same as for tower input Provide calibrated jet finder input Local hadronic scale balances responses better during jet formation in recursive recombination algorithms like Anti-kT and kT Reconstructed tracks Charged stable particles only Resulting jets are incomplete Very useful for characterization of calorimeter jet Large charged pT fraction indicates hadron-rich jet Calorimeter towers filled with cells from topological clusters applies noise suppression to tower signal Topological calorimeter cell clusters locate “blobs of energy” inside the detector following shower and particle flow induced signal structures Drawings by R. Walker (Arizona), inspired by K. Perez (Columbia)

Image Of Jets In Calorimeter

Response & Calibration
Jet Response & Calibration

Calorimeter Jet Response
Electromagnetic energy scale Available for all signal definitions No attempt to compensate or correct signal for limited calorimeter acceptance Global hadronic energy scale All signal definitions, but specific calibrations for each definition Calibrations normalized to reconstruct full true jet energy in “golden regions” of calorimeter Local hadronic energy scale Topological clusters only No jet context – calibration insufficient to recover calorimeter acceptance limitations – no corrections for total loss in dead material and magnetic field charged particles losses)

Jet Energy Scale Final Jet Energy Scale (JES)
Final jet calibration All corrections applied Best estimate of true (particle) jet energy Flat response as function of pT Uniform response across whole calorimeter Relative energy resolution Depends on the calorimeter jet response – calibration applies compensation corrections Resolution improvements by including jet signal features Requires corrections sensitive to measurable jet variables Can use signals from other detectors Determination with simulations Measure residual deviations of the calorimeter jet response from truth jet energy Derive corrections from the calorimeter response at a given scale as function of pT (linearity) and pseudorapidity (uniformity) for all particle jets Use numerical inversion to parameterize corrections Conversion from truth variable dependence of response to reconstructed variable response

Jet Calibration in ATLAS
(2010+ Data Summary)

Default 2010 ATLAS Jet Calibration (1)
Default for first data focuses on “simplicity” EM scale + additional corrections Least algorithmic impact & sensitivity to modeling details Very few correction levels Basic EM scale independently validated with data from Z → ee Basic systematic uncertainty in most calorimeter regions derived independently from jet response No significant improvements in resolution expected Calibration uses only average event environment and jet reponse features More dynamic calibrations under commissioning for data Use hadronic calorimeter scales and jet features GCW, LCW, GS Expect jet energy resolution improvements Corrections are applied jet by jet EM Scale Jet See also D. Schouten’s talk!

Calibration sequence for EM scale jets (1) Pile-up correction from data Average additional energy from pile-up is subtracted EM Scale Jet data Derived from minimum bias data by measuring: ATLAS-CONF

Calibration sequence for EM scale jets (1) Pile-up correction from data (2) Vertex correction from data to improve angular resolution and pT response Jet and constituent directions recalculated from reconstructed primary event vertex EM Scale Jet data data Only jet constituents and jet direction re-calculated after vertex shift – jet energy unchanged!

Calibration sequence for EM scale jets (1) Pile-up correction from data (2) Vertex correction from data to improve angular resolution and pT response (3) Response calibration with MC truth jet Match MC particle jet with simulated calorimeter jet Restores calorimeter jet energy to particle jet reference for given jet finder configuration, physics and detector response modeling EM Scale Jet data data mc Corrects for detector effects & acceptance – parameterized as function of the original calorimeter jet direction ηdet and EM scale energy after pile-up correction

Calibration sequence for EM scale jets (1) Pile-up correction from data (2) Vertex correction from data to improve angular resolution and pT response (3) Response calibration with MC truth jet EM Scale Jet data data ATLAS-CONF ATLAS-CONF mc

Calibration sequence for EM scale jets (1) Pile-up correction from data (2) Vertex correction from data to improve angular resolution and pT response (3) Response calibration with MC truth jet (4) Final direction correction from MC Small correction to reduce bias in direction measurement Introduced by poorly instrumented transition regions in calorimeter EM Scale Jet data data mc mc Correction parameterized as function of detector jet direction and energy EM+JES Jet

Calibration sequence for EM scale jets (1) Pile-up correction from data (2) Vertex correction from data to improve angular resolution and pT response (3) Response calibration with MC truth jet (4) Final direction correction from MC EM Scale Jet data data ATLAS-CONF mc mc EM+JES Jet

???? Expectations for 2011 Data Calibration sequence for scale jets
Starting from locally calibrated (LC) clusters Detector effects unfolded to larger part Pile-up correction from data Very different in presence of pile-up history (50 ns bunch-xing, strategy not yet confirmed) Vertex correction from data to improve angular resolution and pT response Very similar Response calibration with MC truth jet Expected to be significantly smaller due to hadronic energy scale input similar Final direction correction from MC HAD Scale Jet data ???? data mc mc EM+JES Jet

I prefer Cacciari/Salam/Soyez method!
Expectations for 2011 Data Calibration sequence for scale jets Starting from locally calibrated (LC) clusters Detector effects unfolded to larger part Pile-up correction from data Very different in presence of pile-up history (50 ns bunch-xing, strategy not yet confirmed) Vertex correction from data to improve angular resolution and pT response Very similar Response calibration with MC truth jet Expected to be significantly smaller due to hadronic energy scale input similar Final direction correction from MC HAD Scale Jet data I prefer Cacciari/Salam/Soyez method! data mc mc EM+JES Jet

Jet Area Based Pile-up Correction
Principal idea (Cacciari/Salam/Soyez) Cluster the whole event as is with kT/CA Resulting jets depend on distance resolution parameter R = 0.4 seems to be ok No pT cuts on final jets, pT=0 jets allowed Provide combined occupancy measure (~ # pT=0 jets) with transverse energy flow Measure Et density for these jets event by event Focus on low density regime Take median of all densities to reduce dependence on hard part of event Correction similar to tower based approach Use jet area of any hard jet to get pT offset No need for hard jets to be clustered like for event decomposition Use dynamic area definitions as given by FastJet Correction derived from same event And event by event Seems to address jet response dependence on number of primary vertices and (average) pile-up activity very well! M.Cacciari, G. Salam, Pileup subtraction using jet areas, arXiv: v2 [hep-ph]

Principal idea (Cacciari/Salam/Soyez) Cluster the whole event as is with kT/CA Resulting jets depend on distance resolution parameter R = 0.4 seems to be ok No pT cuts on final jets, pT=0 jets allowed Provide combined occupancy measure (~ # pT=0 jets) with transverse energy flow Measure Et density for these jets event by event Focus on low density regime Take median of all densities to reduce dependence on hard part of event Correction similar to tower based approach Use jet area of any hard jet to get pT offset No need for hard jets to be clustered like for event decomposition Use dynamic area definitions as given by FastJet Correction derived from same event And event by event Seems to address jet response dependence on number of primary vertices and (average) pile-up activity very well! Expect dependence of transverse energy flow on pseudo-rapidity from Physics (see, e.g, Bjorken…) Detector effects – signal loss in calorimeter cracks cannot be corrected outside of e.g. jets (no good truth reference!)

Principal idea (Cacciari/Salam/Soyez) Cluster the whole event as is with kT/CA Resulting jets depend on distance resolution parameter R = 0.4 seems to be ok No pT cuts on final jets, pT=0 jets allowed Provide combined occupancy measure (~ # pT=0 jets) with transverse energy flow Measure Et density for these jets event by event Focus on low density regime Take median of all densities to reduce dependence on hard part of event Correction similar to tower based approach Use jet area of any hard jet to get pT offset No need for hard jets to be clustered like for event decomposition Use dynamic area definitions as given by FastJet Correction derived from same event And event by event Seems to address jet response dependence on number of primary vertices and (average) pile-up activity very well! G. Soyez, talk at workshop on Jet reconstruction and spectroscopy at hadron colliders, Pisa, Italy, April 18-19, 2011

Validation & Uncertainties
Jet Reconstruction Validation & Uncertainties

Calibration Validation
Advantages of simulation based jet calibrations Explicit knowledge of “truth” allows understanding of jet signal E.g., true jet energy and direction Factorization allows understanding the individual contributions to calibration Analysis of (nearly) independent error sources and correction quality Improvements in complementary (orthogonal) corrections Note that while effects to be corrected may be correlated, the corrections are derived independently (local hadronic calibration) Challenges for simulation based calibration Need to validate simulation based calibrated signal with data Single particle response – test beam and isolated charged tracks in collision events Effect of calibrations on signal in data and simulation – e.g., signal enhancement Need to validate calibration inputs Same features in data and simulations – cell energy densities, cluster shapes, jet shapes need to be well modeled Inside and outside of jets Need to understand model dependencies Choice of calibration reference involves selection of physics models, detector simulation parameters and geometries, quality of experimental effect modeling (e.g., noise) Variations/exchange of models needed to understand limitations introduced by chosen reference

Validation of Reconstructed Calorimeter Signals (1)
Basic calorimeter signal features enter hadronic calibration Cell energy density in signal weighting Requires very similar distributions in cell energy densities Energy sharing between longitudinal calorimeter samplings Validation of longitudinal shower development – correct energy sharing can enter calibration directly (parameter) and indirectly (classification) More complex variables for cluster based calibration Cluster locations Affects classification and following calibration method Cluster classification Determines calibration strategy and corresponding signal boost Cluster isolation Transfer from single particle calibration to jets Cluster density Classification parametrization Cluster electromagnetic energy scale signal Reference scale for cell weights

Validation & Uncertainties
Jet Reconstruction Validation & Uncertainties

Quality Of MC Based Jet Calibration
Closure test for MC calibration Apply calibrations and corrections to MC calibration sample Expected true energy or pT not perfectly restored after all calibrations and corrections Residual non-closure is part of systematic uncertainty of jet energy scale Differences in energy and pT linearity Same correction factor applied Reconstructed (non-zero) jet masses do not represent expected jet mass well – restoring only energy and direction leads to bias (all plots from ATLAS-CONF )

Jet Energy Scale Uncertainties
Contributions to systematic JES uncertainties in central region of ATLAS [MC] Non-closure of calibration See previous slide [MC] model dependencies Apply calibrations from reference sample to… Different response simulation/Geant4 shower model Detector description variations/material budget & alignment Alternative physics simulation with different underlying event, fragmentation/hadronization, parton shower model… [MC,data] calorimeter response Charged hadrons 0.5 < p < 20 GeV (see next slide) E/p from isolated tracks in collisions Charged hadrons 20 < p < 350 GeV Test beam experiments Basic energy scale and EM response Z → ee in collisions Neutral hadrons Estimates from MC (conservative) High energy particles in jet (p > 400 GeV) Estimates from MC (conservative) Extrapolation to end-cap and forward regions [data] pT balance in QCD di-jet events Constraints the forward energy scale (all plots from ATLAS-CONF )

Single Hadron Response
Determines basic response uncertainties at low energy Measure E/p for isolated tracks in collision events 500 MeV < p < 20 GeV Data/MC response agree very well Larger discrepancies at higher momentum

In-Situ Calibration Validation
Balancing jet pT with electromagnetic system Truth from collision Based on idea that electromagnetic particles are well measured Limits accuracy to precision of photon or electron signal reconstruction Provides interaction (parton) level reference Note that simulation based approaches use particle level reference Can use direct photon production Kinematic reach for jet pT ~ GeV for 1% precision – depends on center of mass energy Relatively large cross-section Background from QCD di-jets – one jet fluctuates into π0 faking photon Can also use Z+jet(s) Cross-section suppressed, but less background – two electron final state cleaner Can also use two muon final state Note specific physics environment Underlying event different from other final states Less radiation in photon/Z hemisphere Often only good reference for quark jets Narrow jets in lower radiation environment balance photon with (mostly) quark jet pT to validate or constrain pT,reco,jet

In-Situ Calibration Validation
Balancing jet pT with electromagnetic system Truth from collision Based on idea that electromagnetic particles are well measured Limits accuracy to precision of photon or electron signal reconstruction Provides interaction (parton) level reference Note that simulation based approaches use particle level reference Can use direct photon production Kinematic reach for jet pT ~ GeV for 1% precision – depends on center of mass energy Relatively large cross-section Background from QCD di-jets – one jet fluctuates into π0 faking photon Can also use Z+jet(s) Cross-section suppressed, but less background – two electron final state cleaner Can also use two muon final state Note specific physics environment Underlying event different from other final states Less radiation in photon/Z hemisphere Often only good reference for quark jets Narrow jets in lower radiation environment balance Z pT reconstructed from decay leptons with quark jet pT to validate or constrain pT,reco,jet

Data Driven JES Corrections: Scale
Absolute response Goal: Correct for energy (pT) dependent jet response Tools: Direct photons, Z+jet(s),… Measurement: pT balance of well calibrated system (photon, Z) against jet in central region Remarks: Usually uses central reference and central jets (region of flat reponse) Concerns: Limit in precision and estimates for systematics w/o well understood simulations not clear Needs corrections to undo out-of-cone etc. to compare to particle level calibrations

Missing Transverse Energy Projections
Missing Transverse Energy Projection Fraction method (MPF) Uses pT balance in photon+jet events to determine jet response Technically on any jet response scale, but most useful if jet signal is corrected for e/h and other (local) detector effects Based on projection of event missing transverse energy (MET) on photon pT direction MET mostly generated by jet response Least sensitive to underlying event and pile-up due to randomization in azimuth Allows to validate the jet energy response Reference can be energy instead of pT Basis of absolute jet energy scale in DZero Also under study for LHC Considerations Perfect balance at parton level perturbed at particle level Parton showering and hadronization, including initial and final state radiation (ISR & FSR) Can be suppressed by selecting back-to-back photon-jet topologies Imperfect calorimeter response generates missing transverse energy Handle for calibration

Missing Transverse Energy Projections
Missing Transverse Energy Projection Fraction method (MPF) Uses pT balance in photon+jet events to determine jet response Technically on any jet response scale, but most useful if jet signal is corrected for e/h and other (local) detector effects Based on projection of event missing transverse energy (MET) on photon pT direction MET mostly generated by jet response Least sensitive to underlying event and pile-up due to randomization in azimuth Allows to validate the jet energy response Reference can be energy instead of pT Basis of absolute jet energy scale in DZero Also under study for LHC Considerations Perfect balance at parton level perturbed at particle level Parton showering and hadronization, including initial and final state radiation (ISR & FSR) Can be suppressed by selecting back-to-back photon-jet topologies Imperfect calorimeter response generates missing transverse energy Handle for calibration ATLAS Simulations ATL-PHYS-PUB (2009)

JES In Situ Validations: Photon-Jet Balance
(from D. Schouten, In-situ measurements of Jet Energy Scale in ATLAS, talk given at “Workshop on Jet Measurements and Spectroscopy”, Pisa, Italy, April 18-19, 2011)

In-situ calibration validation handle
W Mass Spectroscopy In-situ calibration validation handle Precise reference in ttbar events Hadronically decaying W-bosons Jet calibrations should reproduce W-mass Note color singlet source No color connection to rest of collision – different underlying event as QCD Also only light quark jet reference Expected to be sensitive to jet algorithms Narrow jets perform better – as expected CERN-OPEN arXiv: [hep-ex]

JES From W Mass Reconstruction
W boson mass from two jets Clean event sample can be accumulated quickly Original studies for center of mass energy of 14 TeV and luminosity of 1033 cm-2s-1 ~130 clean events/day in ttbar Angular and energy scale component in reconstruction Energy scale dominant arXiv: [hep-ex]

JES From W Mass Reconstruction
W mass from templates Produce W mass distribution templates Use parton or particle level simulations Smear with JES and resolution variations Store W mass distributions as function of smearing parameters Find response and resolution smearing parameters Find best fit template arXiv: [hep-ex] arXiv: [hep-ex]

Biases In W Mass Reconstruction
Boosted W pT boost reduces angle between decay jets Reconstructed mass underestimates true W mass See example below for W boosted into the ATLAS end-cap calorimeter region Pile-up can add energy to the system Not an improvement of the measurement – accidental and thus uncorrelated jet energy shifts lead to shift in reconstructed mass P.Loch and P.Savard, in Proc. of the 7th Conference on Calorimetry in High Energy Physics, Tucson, Arizona, , World Scientific (1998)

JES In Situ Validations
Photon-jet pT balance Validate JES from MC Balance jet pT with well measured photon pT Compare data and MC predictions for central balance Kinematical limitations 20 < pT(Jet) < 300 GeV at 2010 statistics Multi-jet balance Validate leading jet pT in multi-jet final states Balance leading jet pT (> 300 GeV) with several lower pT jets (recoil, individual jet pT < 300 GeV) Assume that recoil system pT is validated by photon+jet/Z+jet ATLAS-CONF

JES In Situ Validations
Photon-jet pT balance Validate JES from MC Balance jet pT with well measured photon pT Compare data and MC predictions for central balance Kinematical limitations 20 < pT(Jet) < 300 GeV at 2010 statistics Multi-jet balance Validate leading jet pT in multi-jet final states Balance leading jet pT (> 300 GeV) with several lower pT jets (recoil, individual jet pT < 300 GeV) Assume that recoil system pT is validated by photon+jet/Z+jet

Di-jet Balancing (from D. Schouten, In-situ measurements of Jet Energy Scale in ATLAS, talk given at “Workshop on Jet Measurements and Spectroscopy”, Pisa, Italy, April 18-19, 2011)

Track Jet Reference Ratio of calorimeter jet/matching track jet pT
Surprisingly well understandable from simulations Average behaviour well constrained in the presence of relative large fluctuations Not applicable jet-by-jet, requires significant statistics in each phase space bin considered Covers pT transition between photon-jet and multi-jet pT balance within tracking acceptance ~200-~600 GeV jet pT Sufficient overlap to avoid gaps in systematic error estimation Track jets also good reference to understand calorimeter response in presence of pile-up Track vertex assignment allows id of tracks from primary collision Needs eta inter-calibration to extend to forward region Larger errors expected – see before!

Use of Track Jets

Combining In-situ Validations
Requirements: Propagate all uncertainties in the data to the final uncertainty Use realistic toy MC including interpolation between and averaging of contributing uncertainties Minimize biases on the shapes of the measured distribution E.g, linear interpolation in pT in small steps (1 GeV) Average data Consider all known correlations (e.g., from toy MC) and minimize error spread as measured by the toy MC HPVTools Software package performing above tasks Originally from muon g-2 analysis

Combining In-situ Validations
Combined systematic uncertainty from several in-situ techniques in ATLAS Relative contribution from any given in-situ technique to the total systematic jet energy scale uncertainty in ATLAS

Different Final States: Quark Jets
ATLAS plots from arXiv: [hep-ex]

Different Final States: Quark Jets
No data - for demonstration only! ATLAS plots from arXiv: [hep-ex]

Other Sources Of JES Uncertainties
Longitudinal jet energy leakage Dangerous – can changes jet pT cross-section shape at high pT Fake compositeness signal Correlated with muon spectrometer hits Not strong correlation expected Insufficient for precise JES Will likely not produce reconstructed tracks, only Helps to tag suspicious jets Suppress suspicious events/jets Careful – real muon may be inside jet b decay Should produce track – cleaner signal inside jet Also background for missing transverse energy!

Calibration Refinement
Using Jet Observables

Jet Calibration Refinement
Explicit use of jet shapes Global sequential calibration Refined calibration applied on top of LC or other scale E.g., in lieu of MC based numerical inversion techniques Promising use of individual jet features Potential to improve jet-by-jet energy measurement – jet energy resolution improvement Works well with few basic jet variables Longitudinal energy sharing Jet width in tracks or from calorimeter Basics for GS calibration: jet response variations as function of several sensitive variables Energy fraction 1st layer Tile Energy fraction 3rd layer EM Energy fraction PreSampler Calorimeter jet width

Other Dedicated Uses Of Track Jets
Find jets in reconstructed tracks ~60% of jet pT, with RMS ~0.3 – not a good kinematic estimator Dedicated 3-dim jet algorithm Cluster track jets in pseudo-rapidity, azimuth, and delta(ZVertex) Match track and calorimeter jet Helps response! ATLAS MC (preliminary) CERN-OPEN

Jet Energy Resolution: Di-jet Balance
In-situ determination Di-jet balance Soft radiation correction Extrapolate 3rd jet pT → 0 Bi-sector method Analyze fluctuations along bi-sectors in transverse pane Suppress radiation contribution ATLAS-CONF ATLAS-CONF

Jet Energy Resolution: Bi-sector
In-situ determination Di-jet balance Soft radiation correction Extrapolate 3rd jet pT → 0 Bi-sector method Analyze fluctuations along bi-sectors in transverse pane Suppress radiation contribution ATLAS-CONF

Jet Energy Resolution Preliminary results
Clear resolution improvement for LCW, GCW, GS No conclusive performance advantage for any of those Data compares to MC at ~10% level Detailed look at performance gain Present baseline calibration EM+JES not ideal –as expected Commissioning of LCW/GCW/GS under way Bi-sector method shown here – results from di-jet balance agree with present errors of the methods

Jets Not From Hard Scatter
Dangerous background for W+n jets cross-sections etc. Lowest pT jet of final state can be faked or misinterpreted as coming from underlying event or multiple interactions Extra jets from UE are hard to handle No real experimental indication of jet source Some correlation with hard scattering? Jet area? No separate vertex Jet-by-jet handle for multiple proton interactions Match tracks with vertices to calorimeter jet Calculate track pT fraction from given vertex Classic indicator for multiple interactions is number of reconstructed vertices in event Tevatron with RMS(z_vertex) ~ 30 cm LHC RMS(z_vertex) ~ 8 cm If we can attach vertices to reconstructed jets, we can in principle identify jets not from hard scattering Limited to pseudorapidities within 2.5! CERN-OPEN

Particle Jets Collection of particles from common source

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